About
I am a Senior Principal Researcher at Microsoft Research AI in the information and data sciences group. My research interests span causal inference, machine learning, and AI’s implications for people and society.
I am working to broaden the use of causal methods for decision-making across many application domains; and improving current applications of correlational machine learning through causal insights. My work uses machine learning methods to scale up conventional causal inference techniques to handle larger-scale and higher-dimensional datasets; adapt causal inference methods to new settings; and improve the robustness and bias of prediction and classification algorithms using causal or causal-inspired approaches.
In the broad area of AI’s implications for society, my work promotes positive applications of AI and strives to mitigate its negative implications. My projects include work at the intersection of security and machine learning, studying new attacks and defenses on security-critical AI-driven systems in an end-to-end setting; questions of data biases and their implications; and infrastructure and methods for developing and maintaining privacy-preserving AI-driven systems; misinformation; and other topics.
I have a strong interest in computational social science questions and social media analyses, especially that require causal understanding of phenomenon in health, mental health; issues of data bias; and understanding how new technologies affect our awareness of the world and enable new kinds of information discovery and retrieval.
My past research has included the reliability, architecture, and operations of distributed systems, including some of the first work to apply machine learning methods to challenges of fault detection and diagnosis in large-scale systems; monitoring and optimization of web applications; and various information retrieval-related tasks, such as entity-linking and using social context to inform document ranking. I received my Ph.D. and my M.S. from Stanford University, and my B.S. in Electrical Engineering and Computer Science from U.C. Berkeley.
Highlights

DoWhy evolves to independent PyWhy model to help causal inference grow
Identifying causal effects is an integral part of scientific inquiry. It helps us understand everything from educational outcomes to the effects of social policies to risk factors for diseases. Questions of cause-and-effect are also critical for the design and data-driven…

Foundations of causal inference and its impacts on machine learning
Many key data science tasks are about decision-making. They require understanding the causes of an event and how to take action to improve future outcomes. Machine learning (ML) models rely on correlational patterns to predict the answer to a question…

DoWhy: Causal Reasoning for Designing and Evaluating Interventions
Today's computing systems can be thought of as interventions in people's work and daily lives. But what are the outcomes of these interventions, and how can we tune these systems for desired outcomes? In this project we are building methods…

Getting efficient with “What-happens-if …”
Causal inference studies the relationship between causes and effects. For example, one kind of question that causal inference can answer is the “What-happens-if …” question. What happens if I take a specific medication? What happens if I raise the price…

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Social data in digital form, including user-generated content, expressed or implicit relations between people, and behavioral traces, are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many,…

Shifts to Suicidal Ideation from Mental Health Content in Social Media
History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness.…